The advantage of quantum mechanics to shift up the ability to econometrically understand extreme tail losses in financial data has become more desirable, especially in cases of Value at Risk (VaR) and Expected Shortfall (ES) predictions. Behind the non-novel quantum mechanism, it does...

In this paper, we extend the parametric approach of VaR estimation that is based upon the application of two transforms, one for handling skewness and other for kurtosis. These transformations restore normality to data when applied in succession. The transforms are well defined and offer an...

In this study, we investigate the relationship between environmental, social, and governance (ESG) disclosures and stock price crash risk. A stock price crash is a dreadful event for market participants. Thus, exploring stock price crash determinants is helpful for investment decisions and risk...

One of the controversies of diversification is that it may not be beneficial to banks, as it tends to increase systemic risk. Recent theoretical and empirical work have addressed this problem. We argue, from a theoretical perspective, that this controversy ultimately depends on how risk is...

In this paper we propose a maximum entropy estimator for the asymptotic distribution of the hedging error for options. Perfect replication of financial derivatives is not possible, due to market incompleteness and discrete-time hedging. We derive the asymptotic hedging error for options under a...

An inherent problem with comparing and ranking competing Value at Risk (VaR) and Expected shortfall (ES) models is that they measure only a single realization of the underlying data generation process. The question is whether there is any significant statistical difference in the performance of...

This paper explores empirically the link between French equities returns Value-at-Risk (VaR) and the state of financial markets cycle. The econometric analysis is based on a simple vector autoregression setup. Using quarterly data from 1970Q4 to 2008Q3, it turns out that the k-year VaR of French...

This paper considers the problem of model uncertainty in the case of multi-asset volatility models and discusses the use of model averaging techniques as a way of dealing with the risk of inadvertently using false models in portfolio management. Evaluation of volatility models is then considered...

We introduce a novel out-of-sample approach to solve a real-time investor's multiperiod portfolio choice problem in a setting with (time-varying) conditional predictability, multiple assets and downside risk control. The method involves defining a discrete set of one-period portfolio allocation...